SCI Publications
2010
B.C. Davis, P.T. Fletcher, E. Bullitt, S. Joshi.
“Population Shape Regression from Random Design Data,” In International Journal of Computer Vision, Vol. 90, No. 1, Note: Marr Prize Special Issue, pp. 255--266. October, 2010.
DOI: 10.1109/ICCV.2007.4408977
S.E. Geneser, J.D. Hinkle, R.M. Kirby, Brian Wang, B. Salter, S. Joshi.
“Quantifying Variability in Radiation Dose Due to Respiratory-Induced Tumor Motion,” In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2010.
DOI: 10.1016/j.media.2010.07.003
S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R.T. Whitaker, the Alzheimers Disease Neuroimaging Initiative (ADNI).
“Manifold modeling for brain population analysis,” In Medical Image Analysis, Special Issue on the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009, Vol. 14, No. 5, Note: Awarded MICCAI 2010, Best of the Journal Issue Award, pp. 643--653. 2010.
ISSN: 1361-8415
DOI: 10.1016/j.media.2010.05.008
PubMed ID: 20579930
L. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva, S. Joshi.
“Image Registration Driven by Combined Probabilistic and Geometric Descriptors,” In Med Image Comput Comput Assist Interv., Vol. 13, No. 2, pp. 602--609. 2010.
PubMed ID: 20879365
L.K. Ha, J. Krüger, S. Joshi, C.T. Silva.
“Multi-scale Unbiased Diffeomorphic Atlas Construction on Multi-GPUs,” In GPU Computing Gems, Vol. 1, 2010.
In this chapter, we present a high performance multi-scale 3D image processing framework to exploit the parallel processing power of multiple graphic processing units (Multi-GPUs) for medical image analysis. We developed GPU algorithms and data structures that can be applied to a wide range of 3D image processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. Our framework helps scientists solve computationally intensive problems which previously required super computing power. To demonstrate the effectiveness of our framework and to compare to existing techniques, we focus our discussions on atlas construction - the application of understanding the development of the brain and the progression of brain diseases.
L.K. Ha, M.W. Prastawa, G. Gerig, J.H. Gilmore, C.T. Silva, S. Joshi.
“Image Registration Driven by Combined Probabilistic and Geometric Descriptors,” In Proceedings of Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Lecture Notes in Computer Science (LNCS), Vol. 6362/2010, pp. 602--609. 2010.
DOI: 10.1007/978-3-642-15745-5_74
Deformable image registration in the presence of considerable contrast differences and large-scale size and shape changes represents a significant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myelination and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrastmeasurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries.We transform intensity patterns into combined probabilistic and geometric descriptors that drive thematching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brainMRIs to two-year old infantMRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposedmethod generates registrations that better preserve the consistency of anatomical structures over time.
Keywords: netl
N.P. Singh, P.T. Fletcher, J.S. Preston, L. Ha, R. King, J.S. Marron, M. Wiener, S. Joshi.
“Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures,” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, Lecture Notes in Computer Science (LCNS), Vol. 6363/2010, pp. 529-537. 2010.
DOI: 10.1007/978-3-642-15711-0_66
PubMed ID: 20879441
2009
P.T. Fletcher PT, S. Venkatasubramanian, S. Joshi.
“The geometric median on Riemannian manifolds with application to robust atlas estimation,” In Neuroimage, Vol. 45, No. 1, pp. S143--S152. March, 2009.
PubMed ID: 19056498
S.E. Geneser, R.M. Kirby, Brian Wang, B. Salter, S. Joshi.
“Incorporating patient breathing variability into a stochastic model of dose deposition for stereotactic body radiation therapy,” In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 688--700. 2009.
PubMed ID: 19694304
S. Gerber, T. Tasdizen, S. Joshi, R.T. Whitaker.
“On the Manifold Structure of the Space of Brain Images,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Springer, pp. 305--312. 2009.
DOI: 10.1007/978-3-642-04268-3_38
PubMed ID: 20426001
L.K. Ha, J. Krüger, T. Fletcher, S. Joshi, C.T. Silva.
“Fast Parallel Unbiased Diffeomorphic Atlas Construction on Multi-Graphics Processing Units,” In Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2009, 2009.
DOI: 0.2312/EGPGV/EGPGV09/041-048
Unbiased diffeomorphic atlas construction has proven to be a powerful technique for medical image analysis, particularly in brain imaging. The method operates on a large set of images, mapping them all into a common coordinate system, and creating an unbiased common template for studying intra-population variability and interpopulation differences. The technique has also proven effective in tissue and object segmentation via registration of anatomical labels. However, a major barrier to the use of this approach is its high computational cost. Especially with the increasing number of inputs and data size, it becomes impractical even with a fully optimized implementation on CPUs. Fortunately, the highly element-wise independence of the problem makes it well suited for parallel processing. This paper presents an efficient implementation of unbiased diffeomorphic atlas construction on the new parallel processing architecture based on Multi-Graphics Processing Units (Multi-GPUs). Our results show that the GPU implementation gives a substantial performance gain on the order of twenty to sixty times faster than a single CPU and provides an inexpensive alternative to large distributed-memory CPU clusters.
H.B. Henninger, S.A. Maas, J.H. Shepherd, S. Joshi, J.A. Weiss.
“Transversely Isotropic Distribution of Sulfated Glycosaminoglycans in Human Medial Collateral Ligament: A Quantitative Analysis,” In Journal of Structural Biology, Vol. 165, pp. 176-183. 2009.
PubMed ID: 19126431
J. Hinkle, P.T. Fletcher, Brian Wang, B. Salter, S. Joshi.
“4D MAP image reconstruction incorporating organ motion,” In Information Processing in Medical Imaging, Lecture Notes in Computer Science LNCS, Vol. 5636, pp. 676--687. 2009.
PubMed ID: 19694303
2008
P.T. Fletcher, S. Venkatasubramanian, S. Joshi.
“Robust Statistics on Riemannian Manifolds via the Geometric Median,” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1--8. 2008.
DOI: 10.1109/CVPR.2008.4587747
R. Kashani, M. Hub, J.M. Balter, M.L. Kessler, L. Dong, L. Zhang, L. Xing, Y. Xie, D. Hawkes, J.A. Schnabel, J. McClelland, S. Joshi, Q. Chen, W. Lu.
“Objective assessment of deformable image registration in radiotherapy: a multi-institution study,” In Medical Physics, Vol. 35, No. 12, pp. 5944--5953. 2008.
PubMed ID: 19175149
D. Merck, G. Tracton, R. Saboo, J. Levy, E. Chaney, S. Pizer, S. Joshi.
“Training models of anatomic shape variability,” In Medical Physics, Vol. 35, No. 8, pp. 3584--3596. 2008.
PubMed ID: 18777919
S. Pizer, M. Styner, T. Terriberry, R. Broadhurst, S. Joshi, E. Chaney, P.T. Fletcher.
“Statistical Applications with Deformable M-Reps,” In Computational Imaging and Vision, Springer, pp. 269--308. 2008.
DOI: 10.1007/978-1-4020-8658-8_9
2007
B. Davis, P.T. Fletcher, E. Bullitt, S. Joshi.
“Population Shape Regression From Random Design Data,” In Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV '07), pp. 1-7. 2007.
P.T. Fletcher, S. Joshi.
“Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data,” In Signal Processing, Vol. 87, No. 2, pp. 250--262. February, 2007.
P.T. Fletcher, S. Powell, N.L. Foster, S. Joshi.
“Quantifying Metabolic Asymmetry Modulo Structure in Alzheimer's Disease,” In Lecture Notes in Computer Science, Springer, pp. 446--457. 2007.
DOI: 10.1007/978-3-540-73273-0_37
PubMed ID: 17633720